The performance of deep learning-based dehazing networks is highly dependent on large and rich paired datasets. However, the collection of paired data consisting of hazy images and clear images in real-world environments is extremely difficult. As a result, existing studies mostly adopted synthetic datasets for training, which leads to insufficient generalization ability of the networks in complex real-world scenarios. Therefore, a CycleGAN-based unsupervised image dehazing network was proposed to solve the problems of color distortion, artifacts, and incomplete dehazing that were prone to occur during the dehazing process of CycleGAN. Firstly, a residual dense connection module was designed to construct the encoder-decoder architecture,while  incorporating spatial and channel residual attention modules.An attention fusion mechanism was introduced in the skip connection section to achieve deep optimization of feature extraction and screening processes.Secondly, a new loss function was designed to effectively balance the visual authenticity and defogging accuracy of generated images.Experimental results show that, compared with baseline methods, the peak signal-to-noise ratio (PSNR) of the dehazed images of the designed network on outdoor synthetic datasets is improved by 22.71%, and the structural similarity (SSIM) value is increased by 6.49%. The image quality is improved  after defogging, and the network can also generate visually realistic foggy images.

In order to improve the operating ability of the system under unbalanced conditions, harmonic suppression was carried out from the perspective of output voltage control. Firstly, for the neutral point clamped(NPC) three-level inverter, fractional order modeling was performed on the controlled object in different coordinate systems to analyze the manifestations of the fundamental and harmonic components in the output voltage at different coordinates, and control was carried out in a two-phase stationary coordinate system. Secondly, to address the issue of low resonance bandwidth in proportional resonance and proportional complex integrators, an improved fractional-order proportional complex integral control (FO-QPCI) algorithm was adopted to suppress the imbalance of output voltage. Finally, the three-level inverter with the rapid control prototype(MT RCP) produced by ModelingTech software as the core controller was  experimentally verified on the physical platform, and results show that the proposed algorithm has good control ability to output voltage under unbalanced conditions and good suppression effect to harmonics.

Taking ship traffic flow as the research object,  a Gaussiana gated recurrent unit (Gaussiana GRU) model based on the Gaussian distribution assumption was proposed to predict the uncertainty distribution of ship traffic flow parameters. Furthermore, a joint probability prediction method for traffic flow density and speed was established by combining with the Copula function. Firstly, based on the characteristics of traffic flow,  a residual GRU structure was designed to enhance the feature extraction capability of GRU through deep stacking, and the Gaussian likelihood function was combined to estimate the probability density distribution of traffic flow. Secondly, in order to solve the "lag" problem of prediction in uncertainty prediction, a point value processing module was introduced to improve the stability and accuracy of model prediction. The joint probability model of traffic flow density and speed was established by using Gaussian Copula function, and the state of three waterways in the Fujiangsha water area were estimated by using sampling method. Experimental results show that compared with the existing models, this method performs well in both point value  and probability density prediction, and can quantify the uncertainty characteristics of ship traffic flow more accurately.

In order to study the factors affecting on the washing effect of chemical tankers, palm oil was selected as a typical non-volatile chemical cargo, stainless steel lined bulkhead chemical tanker was taken as a research object, while a physical experiment system was built and developed a reliable numerical simulation model was developed. The quantitative evaluation model of palm oil tank washing effect was obtained by analyzing the results.During the experiment, it was found that the water jet was mainly influencing factor in the initial stage, and the dissolution of tank washing water in the middle and later stage was the mainly influencing factor. The research results show that, the main factors affecting the tank washing effect are the length and diameter of the nozzle outlet, washing time, temperature of the tank washing water and dynamic pressure.  This work has theoretical reference value for related research and good guidance significance for the practice of chemical tanker washing.

For the collaborative transportation demand between Ro-Ro trailer terminals and inland customers with multiple task types, a tractor scheduling optimization model with time windows, trailer capacity constraints, and considering the inherent correlation between task types and node access order was constructed, and a mixed integer programming model with the goal of minimizing total  transportation costs was established. An improved adaptive large neighborhood search algorithm was proposed to enhance the search efficiency of the destroy and repair operators through a two-stage feasible arc generation strategy and a dynamic weight update mechanism. Numerical experiments demonstrate the correctness of the model and the effectiveness of the algorithm. The experimental results show that the flexible node visit strategy can coordinate the tractor and trailer resources without violating time and capacity constraints, reducing the total transportation cost by 3.16% to 6.47%. Compared with the traditional separation mode, the Ro-Ro separation mode can reduce the total cost by 5.29% to 12.71%,  decreasing the number of tractors required for operation by improving resource utilization. This study can provide a decision-making reference for resource coordination and scheduling between Ro-Ro terminals and the hinterland.

Cryogenic permanent magnet synchronous motors have the advantages of high efficiency and stable operation, making them an ideal choice for the drive components of submerged liquefied natural gas (LNG) pumps. Excessive temperature rise of the drive motor will lead to LNG gasification and affect the operational safety of the submerged pump system. In this paper, a three-dimensional fluid-solid coupling heat transfer model of a 6.5 kW cryogenic permanent magnet synchronous motor was established, and the mathematical model and boundary conditions were given to calculate and analyses the temperature field distribution of the cryogenic permanent magnet synchronous motor by considering the effect of rotor rotation on the flow state of submerged LNG and rotor friction loss. The result show that,when the inlet LNG flow rate is 0.3 m/s, the maximum temperature rise of the motor winding is 41.3°C, and the temperature rise of the permanent magnet and the sheath is in the range of 37.5 ~ 41.7°C, which provides a reference for the optimal design of the subsequent cryogenic permanent magnet synchronous motor.

Using deionized water (70% liquid filling rate) as the working fluid, a comparative experimental study was conducted to investigate the effect of external oscillation sources on the heat transfer performance of closed-loop oscillating heat pipes.Firstly, the  heat transfer performance of closed loop oscillating heat without external oscillation source was studied; Then the heating power was fixed at 25 W and 100 W respectively, and the effect of the oscillation period and amplitude of the external oscillation source on the heat transfer performance of the closed-loop oscillating heat pipe was studied,while the experimental results of the above two methods are compared and analyzed. The results show that, when the heating power is 25 W and 100 W,respectively, the external oscillation source can improve the heat transfer performance of the closed loop oscillating heat pipe. In terms of thermal resistance, the thermal resistance of the closed loop oscillating heat pipe with external oscillation source is smaller than that without external oscillation source, and the maximum reduction of thermal resistance is 68.5% and 48.1%, respectively.In terms of the temperature difference between the heating section and the condensing section, the temperature difference of the closed loop oscillating heat pipe with external oscillation source is lower than that without external oscillation source, and the maximum reduction of the temperature difference is 67.5% and 47.9%, respectively.In terms of the average temperature of the adiabatic section, the temperature of the closed loop oscillating heat pipe with external oscillation source is lower than that without external oscillation source, and the maximum decrease of the average temperature of the adiabatic section is 35.8% and 31.0%, respectively.In terms of start-up performance, the start-up time of the closed loop oscillating heat pipe with external oscillation source is shorter than that without external oscillation source, and the temperature fluctuation in the evaporation section is smaller. 

To address the difficulty in quantifying the impact of ship fires and motion on personnel evacuation,based on platform and real ship experiments of the influence of smoke layer thickness and ship shaking on personnel evacuation,  combined with Anylogic simulation software, a personnel evacuation model considering fire products and shaking effects in ship fire scenarios was constructed. Experimental results show that the platform-based evacuation speed experiments established a relationship between factors like smoke layer thickness and ship shaking conditions and their effects on evacuation speed, with the coupled influence coefficient being the product of their individual coefficients. The three sets of personnel evacuation experiments conducted on the training ship under different smoke layer thicknesses yielded evacuation completion times of 82 s, 78 s, and 85 s, respectively. Compared with the experimental results, the numerical simulations for the corresponding conditions show average error rates of 7.6%, 7.2%, and 7.1%. Numerical simulations show that in a fire scenario without considering fire products, 47 injuries and 6 fatalities might occur, whereas incorporating fire products increased evacuation time by 32.3 s but resulted in zero casualties. When considering roll and pitch motions during a fire, the evacuation times differed by 10.5 s and 33.2 s, respectively, compared to scenarios only accounting for fire products, indicating that pitch motion has a more significant impact on evacuation. This research provides theoretical guidance for personnel evacuation and route selection under ship fire conditions.

Aiming at the problem that traditional water level forecasting models are difficult to effectively capture complex nonlinear dynamics features and affect prediction accuracy,a water level forecasting model based on multi-layer echo kernel state network (ML-EKSN) was proposed. Gaussian kernel functions was used to achieve high-dimensional nonlinear mapping, and hierarchical feature extraction was carried out through multi-layer structure, significantly improving the dynamic modeling ability of the model. Based on the measured data from 7 stations in the Songhua River Basin, ML-EKSN was used to predict the future 7 step water level characteristics based on 30 step historical data.The r esults show that the performance of ML-EKSN is superior to mainstream models such as LSTM, GRU, RVFL, ESN, EKSN,and Transformer, and can provide an efficient and stable solution for predicting water  levels  in inland waterways and has good practical application value.

Aiming at the problems of low denitrification rate and high consumption of oxidant concentration during the wet treatment of ship exhaust gas, a novel denitrification method based on the synergistic effect of hydraulic cavitation and cobalt ferrite nanoparticles catalyzing potassium peroxymonosulfate (PMS) was proposed, and the effects of factors such as solution concentration, temperature, pressure difference, catalyst concentration, and seawater alkali activation on  NOx removal rate were studied through experiments. The experimental results show that, under the fresh water condition, the effect of each factor on the denitrification efficiency underscores  a trend of promotion first and then inhibition. The optimal conditions are derived as follows:a PMS concentration of 0.4 mol/L, a pressure difference of 400 kPa, a reaction time of 0.962 s, a cobalt ferrite nanoparticles concentration of 0.1 g/L, and a solution temperature of 55°C. Under these conditions, a NOx denitrification rate of 90.07% was achieved. Under the simulated seawater condition, after alkali activation, the denitrification efficiency is further increased to a maximum of 96.42%. This study provides a new method for efficient denitrification of ship exhaust gas.